IDL - International Digital Library Of Technology & Research Volume 1, Issue 5, May 2017
Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
Effect of Data Size on Feature Set Using Classification in Health Domain Uttham H1*, Gowramma2 1 PG-Student, 2Associate Professor, Dept. Computer Science & Engineering, D.B.I.T, Banglore, Karnataka, India. 1* utthamhmanju@gmail.com,2gowramma@gmail.com.
ABSTRACT: In health domain, the major critical issue is prediction of disease in early stage. Prediction of disease is mainly based on the experience of physician so many machine learning approach contribute their work in the prediction of disease. In existing approaches, either prediction or feature selection has been concentrated. The aim of this paper is to present the effect of data size and set of features in the prediction of disease in health domain using NaĂŻve Bayes. This shows how each attribute or combination of attribute behaves on different size of dataset. Keywords: Machine Learning, Classification, NaĂŻve Bayes, feature selection. 1. INTRODUCTION In health, domain diagnosis of disease is
the experience. If the physician has more
very challenging task. Earlier prediction can
experience, then he may predict well. if the
made based on some lab test. Using this lab
physician has less experience then he may
test report the physician will decide whether
predict wrongly.to overcome from this
the patient has disease or not but prediction
problem
of disease by physician mainly depend on
approaches like KNN, SVM, ANN to
IDL - International Digital Library
1|P a g e
machine
learning
has
many
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